Alexander Singer1, Sari Yakubovich2, Andrea L Kroeker3, Brenden Dufault4, Roberto Duarte2, Alan Katz5. 1. Department of Family Medicine, University of Manitoba, Winnipeg, Manitoba, Canada asinger@sbgh.mb.ca. 2. Department of Family Medicine, University of Manitoba, Winnipeg, Manitoba, Canada. 3. Department of Immunology, University of Manitoba, Winnipeg, Manitoba, Canada. 4. George and Fay Yee Center for Healthcare Innovation; College of Medicine, University of Manitoba, Canada. 5. Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Abstract
OBJECTIVE: To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness. METHODS: A retrospective analysis of primary care EMR data quality related to seven chronic diseases (hypertension, diabetes, asthma, congestive heart failure, coronary artery disease, hypothyroidism, and chronic obstructive pulmonary disorder) in Manitoba, Canada. We included 119 practices in 18 primary care clinics across urban and rural Manitoba. The main outcome measure was EMR problem list completeness. Completeness was measured by comparing the number of EMR-documented diagnoses to the number of billings associated with each disease. We calculated odds ratios for the effect of clinic patient load and salary type on EMR problem list completeness of the 7 chronic diseases. RESULTS: Completeness of EMR problem list for each disease varied widely among clinics. Factors that significantly affected EMR problem list completeness included the primary care provider, the patient load, and the clinic's funding and organization model (ie, salaried, fee-for-service, or residency training clinics). Average rates of completeness were: hypertension, 72%; diabetes, 80%; hypothyroidism, 63%; asthma, 56%; chronic obstructive pulmonary disorder, 43%; congestive heart failure, 54%; and coronary artery disease, 64%. CONCLUSION: This study demonstrates the high variability but generally low quality of problem lists (health condition records) related to 7 common chronic diseases in EMRs. There are systematic physician- and clinic-level factors associated with low data quality completeness. This information may be useful to support improvement in EMR data quality in primary care.
OBJECTIVE: To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness. METHODS: A retrospective analysis of primary care EMR data quality related to seven chronic diseases (hypertension, diabetes, asthma, congestive heart failure, coronary artery disease, hypothyroidism, and chronic obstructive pulmonary disorder) in Manitoba, Canada. We included 119 practices in 18 primary care clinics across urban and rural Manitoba. The main outcome measure was EMR problem list completeness. Completeness was measured by comparing the number of EMR-documented diagnoses to the number of billings associated with each disease. We calculated odds ratios for the effect of clinic patient load and salary type on EMR problem list completeness of the 7 chronic diseases. RESULTS: Completeness of EMR problem list for each disease varied widely among clinics. Factors that significantly affected EMR problem list completeness included the primary care provider, the patient load, and the clinic's funding and organization model (ie, salaried, fee-for-service, or residency training clinics). Average rates of completeness were: hypertension, 72%; diabetes, 80%; hypothyroidism, 63%; asthma, 56%; chronic obstructive pulmonary disorder, 43%; congestive heart failure, 54%; and coronary artery disease, 64%. CONCLUSION: This study demonstrates the high variability but generally low quality of problem lists (health condition records) related to 7 common chronic diseases in EMRs. There are systematic physician- and clinic-level factors associated with low data quality completeness. This information may be useful to support improvement in EMR data quality in primary care.
Authors: Ron C Li; Trit Garg; Tony Cun; Lisa Shieh; Gomathi Krishnan; Daniel Fang; Jonathan H Chen Journal: J Am Med Inform Assoc Date: 2018-05-01 Impact factor: 4.497
Authors: Robert W Voss; Teresa D Schmidt; Nicole Weiskopf; Miguel Marino; David A Dorr; Nathalie Huguet; Nate Warren; Steele Valenzuela; Jean O'Malley; Ana R Quiñones Journal: J Am Med Inform Assoc Date: 2022-04-13 Impact factor: 4.497